Browsing Computer Science and Automation (CSA) by thesis submitted date"2020"
Now showing items 1-20 of 20
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Algorithms for Challenges to Practical Reinforcement Learning
Reinforcement learning (RL) in real world applications faces major hurdles - the foremost being safety of the physical system controlled by the learning agent and the varying environment conditions in which the autonomous ... -
Algorithms for Fair Decision Making: Provable Guarantees and Applications
The topic of fair allocation of indivisible items has received significant attention because of its applicability in several real-world settings. This has led to a vast body of work focusing on defining appropriate fairness ... -
Algorithms for Social Good in Online Platforms with Guarantees on Honest Participation and Fairness
Recent decades have seen a revolution in the way people communicate, buy products, learn new things, and share life experiences. This has spurred the growth of online platforms that enable users from all over the globe to ... -
Algorithms for Stochastic Optimization, Statistical Estimation and Markov Decision Processes
Stochastic approximation deals with the problem of finding zeros of a function expressed as an expectation of a random variable. In this thesis we propose convergent algorithms for problems in optimization, statistical ... -
Battle of Bandits: Online Learning from Subsetwise Preferences and Other Structured Feedback
The elicitation and aggregation of preferences is often the key to making better decisions. Be it a perfume company wanting to relaunch their 5 most popular fragrances, a movie recommender system trying to rank the most ... -
Constant-rate Non-malleable Codes and their Applications
Non-malleable codes(NMC) introduced by Dziembowski, Pietrzak and Wichs in ITCS 2010, provide powerful security guarantees where error-correcting codes can not provide any guarantee: a decoding of tampered codeword is ... -
Decision Making under Uncertainty : Reinforcement Learning Algorithms and Applications in Cloud Computing, Crowdsourcing and Predictive Analytics
In this thesis, we study both theoretical and practical aspects of decision making, with a focus on reinforcement learning based methods. Reinforcement learning (RL) is a form of semi-supervised learning in which the agent ... -
Deep Learning for Bug Localization and Program Repair
In this thesis, we focus on the problem of program debugging and present novel deep learning based techniques for bug-localization and program repair. Deep learning techniques have been successfully applied to a variety ... -
Embedding Networks: Node and Graph Level Representations
Graph neural networks gained significant attention for graph representation and classification in the machine learning community. For graph classification, different pooling techniques are introduced, but none of them has ... -
Extending Program Analysis Techniques to Web Applications and Distributed Systems
Web-based applications and distributed systems are ubiquitous and indispensable today. These systems use multiple parallel machines for greater functionality, and efficient and reliable computation. At the same time they ... -
FA RCU: Fault Aware Read-Copy-Update
Deferred freeing is the fundamental technique used in Read-Copy-Update (RCU) synchronization technique where reclamation of resources is deferred until the completion of all active RCU read-side critical sections. We observe ... -
Hypergraph Network Models: Learning, Prediction, and Representation in the Presence of Higher-Order Relations
The very thought about “relating” objects makes us assume the relation would be “pairwise”, and not of a “higher-order” — involving possibly more than two of them at a time. Yet in reality, higher-order relations do exist ... -
Model Extraction and Active Learning
Machine learning models are increasingly being offered as a service by big companies such as Google, Microsoft and Amazon. They use Machine Learning as a Service (MLaaS) to expose these machine learning models to the ... -
Model Extraction Defense using Modified Variational Autoencoder
Machine Learning as a Service (MLaaS) exposes machine learning (ML) models that are trained on confidential datasets to users in the form of an Application Programming Interface (API). Since the MLaaS models are deployed ... -
Novel Neural Architecture for Multi-Hop Question Answering
Natural language understanding has been one of the key drivers responsible for advancing the eld of AI. To this end, automated Question Answering (QA) has served as an effective way of measuring the language understanding ... -
On Learning and Lower Bound Problems Related to the Iterated Matrix Multiplication Polynomial
The iterated matrix multiplication polynomial (IMM) of width w and length d is the 1x1 entry in the product of d square matrices of size w. The w^2d entries in the d matrices are distinct variables. In this thesis, we study ... -
On the Round Complexity Landscape of Secure Multi-party Computation
In secure multi-party computation (MPC), n parties wish to jointly perform a computation on their private inputs in a secure way, so that no adversary corrupting a subset of the parties can learn more information than their ... -
Representing Networks: Centrality, Node Embeddings, Community Outliers and Graph Representation
Networks are ubiquitous. We start our technical work in this thesis by exploring the classical concept of node centrality (also known as influence measure) in information networks. Like clustering, node centrality is also ... -
Towards Secure and Efficient Realization of Pairing-Based Signatures from Static Assumptions.
Bilinear pairing defined over elliptic curve group was first used to design novel cryptosystem in 2000. Since then a large number of cryptosystems has been proposed in pairing-based cryptography (PBC). The main tool for ... -
Verification of a Generative Separation Kernel
A Separation Kernel is a small specialized microkernel that provides a sand-boxed execution environment for a given set of processes (also called \subjects"). The subjects may communicate only via declared memory channels, ...